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The efficiency of collaborative mobile robot applications is influenced by the inherent uncertainty introduced by humans' presence and active participation. This uncertainty stems from the dynamic nature of the working environment, various external factors, and human performance variability. The observed makespan of an executed plan will deviate from any deterministic estimate. This raises questions about whether a calculated plan is optimal given uncertainties, potentially risking failure to complete the plan within the estimated timeframe. This research addresses a collaborative task planning problem for a mobile robot serving multiple humans through tasks such as providing parts and fetching assemblies. To account for uncertainties in the durations needed for a single robot and multiple humans to perform different tasks, a probabilistic modeling approach is employed, treating task durations as random variables. The developed task planning algorithm considers the modeled uncertainties while searching for the most efficient plans. The outcome is a set of the best plans, where no plan is better than the other in terms of stochastic dominance. Our proposed methodology offers a systematic framework for making informed decisions regarding selecting a plan from this set, considering the desired risk level specific to the given operational context.

In this work, we investigate a task planning problem for assigning and planning a mobile robot team to jointly perform a kitting application with alternative task locations. To this end, the application is modeled as a Robot Task Scheduling Graph and the planning problem is modeled as a Mixed Integer Linear Program (MILP). We propose a heuristic approach to solve the problem with a practically useful performance in terms of scalability and computation time. The experimental evaluation shows that our heuristic approach is able to find efficient plans, in comparison with both optimal and non-optimal MILP solutions, in a fraction of the planning time.

The surge in the development and adoption of Electric Vehicles (EVs) globally is a trend many countries are paying close attention to. This inevitably means that a significant number of EV batteries will soon reach their End-of-Life (EoL). This looming issue reveals a notable challenge: there's currently a lack of sustainable strategies for managing Lithium-ion Batteries (LiBs) when they reach their EoL stage. The process of disassembling these battery packs is challenging due to their intricate design, involving several different materials and components integrated tightly for performance and safety. Consequently, effective disassembly and subsequent recycling procedures require highly specialized methods and equipment, and involve significant safety and health risks. Moreover, existing recycling technologies often fail to recover all valuable and potentially hazardous materials, leading to both economic and environmental loss. This paper provides an overview and analysis of possible challenges arising in the domain of automated battery disassembly and recycling of EV batteries that reached their EoL. We provide insight into the disassembly process as well as optimization of the disassembly sequence with the goal of minimizing the overall cost and environmental footprint.

Multi-agent systems can be prone to failures during the execution of a mission, depending on different circumstances, such as the harshness of the environment they are deployed in. As a result, initially devised plans for completing a mission may no longer be feasible, and a re-planning process needs to take place to re-allocate any pending tasks. There are two main approaches to solve the re-planning problem (i) global re-planning techniques using a centralized planner that will redo the task allocation with the updated world state and (ii) decentralized approaches that will focus on the local plan reparation, i.e., the re-allocation of those tasks initially assigned to the failed robots, better suited to a dynamic environment and less computationally expensive. In this paper, we propose a hybrid approach, named GLocal, that combines both strategies to exploit the benefits of both, while limiting their respective drawbacks. GLocal was compared to a planner-only, and an agent-only approach, under different conditions. We show that GLocal produces shorter mission make-spans as the number of tasks and failed agents increases, while also balancing the tradeoff between the number of messages exchanged and the number of requests to the planner.

With the rapidly growing use of Multi-Agent Systems (MASs), which can exponentially increase the system complexity, the problem of planning a mission for MASs became more intricate. In some MASs, human operators are still involved in various decision-making processes, including manual mission planning, which can be an ineffective approach for any non-trivial problem. Mission planning and re-planning can be represented as a combinatorial optimization problem. Computing a solution to these types of problems is notoriously difficult and not scalable, posing a challenge even to cutting-edge solvers. As time is usually considered an essential resource in MASs, automated solvers have a limited time to provide a solution. The downside of this approach is that it can take a substantial amount of time for the automated solver to provide a sub-optimal solution. In this work, we are interested in the interplay between a human operator and an automated solver and whether it is more efficient to let a human or an automated solver handle the planning and re-planning problems, or if the combination of the two is a better approach. We thus propose an experimental setup to evaluate the effect of having a human operator included in the mission planning and re-planning process. Our tests are performed on a series of instances with gradually increasing complexity and involve a group of human operators and a metaheuristic solver based on a genetic algorithm. We measure the effect of the interplay on both the quality and structure of the output solutions. Our results show that the best setup is to let the operator come up with a few solutions, before letting the solver improve them.

Applications are becoming increasingly data-intensive, requiring significant computational resources to meet their demand. Cloud-based services are insufficient to meet such demand, leading to a shift of the computation towards the devices closer to the edge of the network, leading to the emergence of an Edge-to-Cloud computing Continuum (E2C). An application can offload part of its computation toward the E2C. The allocation of applications to a set of available computing nodes is a challenging problem, as the allocation needs to take into account several factors, including the application requirements and demands as well as the optimization of the resource utilization in the E2C infrastructure and the minimization the CO2 footprint of the executed applications. Control and optimization techniques provide a vast array of tools for optimizing the Edge-to-Cloud continuum's management. This paper provides a mathematical formulation for the application offloading with specific requirements in the cloud computing domain. The problem is modeled as integer linear programming and constraint programming models and implemented in commercially available software. Finally, we provide the results of performed comparison between the two models.

Intelligent transportation systems face various challenges, including traffic congestion, environmental pollution, and inefficient transportation management. Optimizing routes and schedules for efficient delivery of goods and services can mitigate the aforementioned problems. Many transportation and routing problems can be modeled as variants of the Traveling Salesmen Problem (TSP) depending on the specific requirements of the scenario at hand. This means that to efficiently solve the routing problem, all locations have to be visited by the available salesmen in a way that minimizes the overall makespan. This becomes a non-trivial problem when the number of salesmen and locations to be visited increases. The problem at hand is modeled as a special TSP variant, called Extended Colored TSP (ECTSP). It has additional constraints when compared to the classical TSP, which further complicates the search for a feasible solution. This work proposes a new metaheuristic approach to efficiently solve the ECTSP. We compare the proposed approach to existing solutions over a series of test instances. The results show a superior performance of our metaheuristic approach with respect to the state of the art, both in terms of solution quality and algorithm's runtime.

Multi-agent systems have received a tremendous amount of attention in many areas of research and industry, especially in robotics and computer science. With the increased number of agents in missions, the problem of allocation of tasks to agents arose, and it is one of the most fundamental classes of problems in robotics, formally known as the Multi-Robot Task Allocation (MRTA) problem. MRTA encapsulates numerous problem dimensions, and it aims at providing formulations and solutions to various problem configurations, i.e., complex multi-agent missions. One dimension of the MRTA problem has not caught much of the research attention. In particular, problem configurations including Multi-Task (MT) robots have been neglected. However, the increase in computational power, in robotic systems, has allowed the utilization of parallel task execution. This in turn had the benefit of allowing the creation of more complex robotic missions; however, it came at the cost of increased problem complexity. Our contribution to the aforementioned domain can be grouped into three categories. First, we model the problem using two different approaches, Integer Linear Programming and Constraint Programming. With these models, we aim at filling the gap in the literature related to the formal definition of MT robot problem configuration. Second, we introduce the distinction between physical and virtual tasks and their mutual relationship in terms of parallel task execution. This distinction allows the modeling of a wider range of missions while exploiting possible parallel task execution. Finally, we provide a comprehensive performance analysis of both models, by implementing and validating them in CPLEX and CP Optimizer on the set of problems. Each problem consists of the same set of test instances gradually increasing in complexity, while the percentage of virtual tasks in each problem is different. The analysis of the results includes exploration of the scalability of both models and solvers, the effect of virtual tasks on the solvers' performance, and overall solution quality.

The use of multiagent systems (MASs) in real-world applications keeps increasing, and diffuses into new domains, thanks to technological advances, increased acceptance, and demanding productivity requirements. Being able to automate the generation of mission plans for MASs is critical for managing complex missions in realistic settings. In addition, finding the right level of abstraction to represent any generic MAS mission is important for being able to provide general solution to the automated planning problem. In this article, we show how a mission for heterogeneous MASs can be cast as an extension of the traveling salesperson problem (TSP), and we propose a mixed-integer linear programming formulation. In order to solve this problem, a genetic mission planner (GMP), with a local plan refinement algorithm, is proposed. In addition, the comparative evaluation of CPLEX and GMP is presented in terms of timing and optimality of the obtained solutions. The algorithms are benchmarked on a proposed set of different problem instances. The results show that, in the presence of timing constraints, GMP outperforms CPLEX in the majority of test instances.

Multi-Agent Systems (MASs) have been utilized in various settings and frameworks, and have thus been successfully applied in many applications to achieve different goals. It has been shown that MASs are more cost-effective as compared to building a single agent with all the capabilities a mission may require. Moreover, the cost is not the only driving factor for the adoption of MASs, e.g., safety is another important aspect: Deploying a group of agents, in a harsh or extreme environment, instead of a human team decreases the safety risks. Furthermore, MASs offer more flexibility and robustness when compared to a single-agent solution. The flexibility comes from dividing resources into separate groups, while robustness comes from the fact that a critical error in one agent does not necessarily endanger the success of a mission. Note that a mission may have many different constraints and aspects, however, the most trivial case has a single agent and a single task. 

These kinds of missions can be planned by a human operator, overseeing a mission, without the need for an automated planner. On the other hand, more complex missions, that are utilizing a large number of heterogeneous agents and tasks, as well as constraints (precedence, synchronization, etc.) are not that trivial to plan for a human operator. These complex problems pose a great challenge to making a feasible plan, let alone the best possible one. Moreover, the increase in the power of available computing platforms in robotic systems has allowed the utilization of parallel task execution. More specifically, it allowed for possible parallelism in sensing, computation, motion, and manipulation tasks. This in turn had the benefit of allowing the creation of more complex robotic missions. However, it came at the cost of increased complexity for the optimization of the task allocation problem. To circumvent these issues, an automated planner is necessary. These types of problems are notoriously difficult to solve, and it may take too long for an optimal plan to be found. Therefore, a balance between optimality and computation time taken to produce a plan become very important.

This thesis deals with the formal definition of two particular Multi-Robot Task Allocation (MRTA) problem configurations used to represent multi-agent mission planning problems. More specifically, the contribution of this thesis can be grouped into three categories. 

Firstly, this work proposes a model to represent different problem configurations, also referred to as missions, in a structured way. This model is called TAMER, and it also allows the addition of new dimensions in a more systematic way, expanding the number of problems that can be described compared to previously proposed MRTA taxonomies.

Secondly, this thesis defines and provides two different problem formulations, in a form of Mixed-Integer Linear Problem formulation, of the Extended Colored Travelling Salesman Problem (ECTSP). These models are implemented and verified in the CPLEX optimization tool on the selected problem instances. In addition, a sub-optimal approach to solving these complex problems is devised. Proposed solutions are based on the Genetic Algorithm (GA) approach, and they are compared to the solutions obtained by state-of-the-art (and state-of-practice) solvers, i.e., CPLEX. The advantage of using GA for planning over classical approaches is that it has better scalability that enables it to find solutions for large-scale problems. Although those solutions are, in the majority of cases, sub-optimal they are obtained much faster than with other exact methods. Another advantage is represented in a form of "anytime stop" option. In time-critical operations, it is important to have the option to stop the planning process and use the sub-optimal solution when it is required. 

Lastly, this work addresses the one dimension of the MRTA problem that has not caught much of the research attention in the past. In particular, problem configurations including Multi-Task (MT) robots have been neglected. To overcome the aforementioned problem, first, the cases in which task parallelism may be achieved have been defined. In addition, the distinction between physical and virtual tasks and their mutual relationship in terms of parallel task execution has been introduced. Two models have been proposed and compared. The first one is expressed as ILP and implemented in the CPLEX optimization tool. The other one is defined as a Constraint Programming (CP) model and implemented in CP optimization tools. Both solvers have been evaluated on a series of problem instances.